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所在平台: Udemy |
课程主页: https://www.udemy.com/course/r-coding-for-data-analysts-from-beginner-to-advanced/
课程评论：没有评论
课程名称：R编程数据分析师：从初学者到高级 课程概述： 本课程是一个基本的R编程课程，专为有志于成为数据分析师的初学者设计，涵盖了编程语言的基础知识（R是数据分析领域最知名和最广泛使用的语言之一），以及描述统计的使用。课程结束时，学生将能够创建、导入、操作和管理数据集。 课程开始时，我们将设置工作环境，包括下载、安装并使用R的重要工具，如RStudio。接下来，我们将学习创建对象，了解R的基本结构，如向量、矩阵、列表和数据框。在熟悉这些数据结构的创建和操作、提取元素及本地保存后，我们将学习循环的使用和函数的创建。 在后续内容中，我们将探讨一系列实用主题，如设置工作目录、安装和检索包、获取数据的信息、查找测试数据集及如何获取函数帮助。在数据分析中，数据框（变量x案例）是常见的结构，我们将学习如何从计算机或互联网上导入数据框到R中，同时介绍适合此目的的多种函数和包，如Excel、.csv、.txt或JSON格式的数据导入。 接下来课程将重点讲解数据的操作，包括创建新变量、数据聚合、横向和纵向排序，以及如何合并两个数据集。为此，我们将使用一些特定的包和函数，如dplyr、tidyr或reshape2。我们还将简要介绍如何与数据库进行接口，以及使用其他包来简化大数据集的管理。 R在统计学领域也非常重要，因此我们将学习一些基本函数，如计算行或列的平均值，以及描述统计领域中最常用的统计函数，如均值、中位数、众数、标准差、分布展示等。 在数据分析过程中，我们经常需要创建图表来解释数据和分析，因此课程中将专门设置一个模块，介绍如何使用基本库和ggplot2包来创建图表。在课程的最后几节中，我们将学习如何创建和导出报告及幻灯片，总结所学习的主题和函数，并查看支持材料。 课程的所有章节都配有编码练习、视频和解决方案脚本。学员可通过测验和实操测试来检验自己的知识，难度逐步递增。
This basic programming course with R for aspiring data analysts is designed to accompany a beginner in programming, from the basics of the programming language (one of the best known and most widely used in the field of data analysis) to the use of descriptive statistics.At the end of this course the student will be able to create, import, manipulate and manage datasets. The course starts with setting up the working environment: we will see how to download, install and use some of the most important tools for using R, such as RStudio. We will then move on to the creation of objects: R is based on certain structures that we need to know, such as vectors, matrices, lists and dataframes. Once we understand how to create and manipulate these data structures, extract elements from them and save them locally on the computer, we will move on to the use of loops and the creation of functions. In the next section, we will look at a number of useful topics: how to set up a working directory, how to install and retrieve a package, how to get information about data, where to find datasets for testing, and how to get help with a function.When analysing data, one sooner or later comes across dataframes known as variable x-cases. We will therefore see how to import a dataframe from your computer, or from the internet, into R. There are many functions that are suitable for this purpose, and many packages that are useful for importing data that is in some particular format, such as the formats for Excel,.csv,.txt or JSON.We will then see how to manipulate data, create new variables, aggregate data, sort them horizontally and longitudinally, and merge two datasets. To do this, we will use some specific packages and functions, such as dplyr, tidyr or reshape2. We will also briefly see how to interface with a database and use other packages to streamline the management of somewhat larger datasets. R is also a very important language in the field of statistics. We will therefore learn some of the basic functions, such as calculating averages per row or per column, and the most common statistical functions in the field of descriptive statistics, such as mean, median, fashion, standard deviation, displaying the distribution and more.When it comes to data analysis, we will often find ourselves creating graphs to explain our data and analyses. For this reason, we devote a section of the course to seeing how to create graphs with both the functions of the basic library and the ggplot2 package. In the last lessons of the course, we will see how to create and export reports and slides, summarise the topics we have seen and the functions we have used, and see the supporting material.All sections of the course are accompanied by coding exercises and videos and scripts with solutions. You can test your knowledge with quiz and practical test with increasing levels of difficulty.
